Probabilistic programming: machine learning via inference algorithms

Larry Hardesty, writing for Phys.org, speaks with Tejas Kulkarni, contributor to a new paper about probabilistic programming for scene perception:

In a probabilistic programming language, the heavy lifting is done by the inference algorithm—the algorithm that continuously readjusts probabilities on the basis of new pieces of training data. In that respect, Kulkarni and his colleagues had the advantage of decades of machine-learning research. Built into Picture are several different inference algorithms that have fared well on computer-vision tasks. Time permitting, it can try all of them out on any given problem, to see which works best.

Moreover, Kulkarni says, Picture is designed so that its inference algorithms can themselves benefit from machine learning, modifying themselves as they go to emphasize strategies that seem to lead to good results.

I think Kulkarni illustrates the idea best when he says, “When you think about probabilistic programs, you think very intuitively when you’re modeling. You don’t think mathematically.”